Neural networks with chaotic baseline behavior are interesting for their experimental bases in both biological relevancy and engineering applicability. In the engineering case, the literature still lacks a robust study of the interrelationship between particular chaotic baseline network dynamics and 'online' or 'driving' inputs. We ask the question, for a particular neural network with chaotic baseline behavior, what periodic inputs of minimal magnitude have a stabilizing effect on network dynamics? A genetic algorithm is developed for the task. A systematic comparison of different genetic operators is carried out where each operator-combination is ranked by the optimality of solutions found. The algorithm reaches acceptable results and _finds input sequences with largest elements on the order of 10^3. Lastly, an illustration of the complexity of the fitness space is produced by brute-force sampling period-2 inputs and plotting a fitness map of their stabilizing effect on the network.